Large Language Model Consensus Substantially Improves the Cell Type Annotation Accuracy for scRNA-seq Data

Yang, C.; Zhang, X.; Chen, J.

doi:10.1101/2025.04.10.647852

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bioRxiv · 10.1101/2025.04.10.647852

Large Language Model Consensus Substantially Improves the Cell Type Annotation Accuracy for scRNA-seq Data

Yang, C.·Zhang, X.·Chen, J.

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Abstract

Different large language models (LLMs) have the potential to complement one another. We introduce an iterative multi-LLM consensus framework for annotating single-cell RNA sequencing data. This framework outperforms the best state-of-the-art method by nearly 15% in mean accuracy (77.3% vs 61.3%) across 50 diverse datasets from 26 tissues, encompassing over 8 million cells. By leveraging cross-model deliberation, our framework quantifies uncertainty, identifies ambiguous clusters for expert review, provides transparent reasoning chains, and minimizes the effort and expertise needed for cell type annotation in large-scale studies. Additionally, our framework enables users to seamlessly integrate new LLMs.

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Yang, C., Zhang, X., Chen, J.. 2025-04-17. Large Language Model Consensus Substantially Improves the Cell Type Annotation Accuracy for scRNA-seq Data. https://doi.org/10.1101/2025.04.10.647852

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